WO2019074967A1 - Systèmes et procédés d'identification et d'authentification de travaux artistiques - Google Patents

Systèmes et procédés d'identification et d'authentification de travaux artistiques Download PDF

Info

Publication number
WO2019074967A1
WO2019074967A1 PCT/US2018/055076 US2018055076W WO2019074967A1 WO 2019074967 A1 WO2019074967 A1 WO 2019074967A1 US 2018055076 W US2018055076 W US 2018055076W WO 2019074967 A1 WO2019074967 A1 WO 2019074967A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
image data
acquired
quantitative
digital fingerprint
Prior art date
Application number
PCT/US2018/055076
Other languages
English (en)
Inventor
G. MCCONNELL Curtis
Doug KOMEN
Daniel I. BODENHEIMER
Original Assignee
Great Masters Art Authentication, Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Great Masters Art Authentication, Llc filed Critical Great Masters Art Authentication, Llc
Priority to US16/754,875 priority Critical patent/US11113554B2/en
Priority to EP18866860.2A priority patent/EP3695379A4/fr
Publication of WO2019074967A1 publication Critical patent/WO2019074967A1/fr
Priority to US17/404,429 priority patent/US11756322B2/en
Priority to US18/361,165 priority patent/US20240161526A1/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/22—Character recognition characterised by the type of writing
    • G06V30/224—Character recognition characterised by the type of writing of printed characters having additional code marks or containing code marks
    • G06V30/2253—Recognition of characters printed with magnetic ink
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G06T7/42—Analysis of texture based on statistical description of texture using transform domain methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/143—Sensing or illuminating at different wavelengths
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/50—Constructional details
    • H04N23/54—Mounting of pick-up tubes, electronic image sensors, deviation or focusing coils

Definitions

  • This patent document relates to quantitative imaging and chemical analysis techniques for quantitative analysis of physical objects including artistic works, high-value artifacts, collectibles, luxury products and high-end consumer goods.
  • Art is the manifestation of human expression or creative skill and imagination, typically in a visual format such as painting or sculpture, an audio format such as music, or a mixture of both such as a performance, in which the produced artistic works are for appreciation and enjoyment for their attributes, beauty, message, meaning, significance, symbolism, and/or other aspects.
  • a painting is typically assembled by applying a form of paint, pigment or other color medium to a support surface, e.g., such as a fabric base or a wooden panel.
  • the support surface typically includes a ground layer, which provides a smooth surface for receiving the paint and absorbing excess binding media of the paint.
  • the top layers of a painting have a coating applied to provide saturation and to protect the paint underneath from dirt, abrasion and moisture.
  • a system for identifying and authenticating an object includes an image data acquisition platform to acquire image data from an object in multiple electromagnetic (EM) spectrums along a coordinated array of sample regions of the object; a data processing device, in communication with the image data acquisition platform, to analyze the acquired image data of the object and produce a quantitative data set that represents specific
  • EM electromagnetic
  • the data processing device is configured to (i) identify the object by creating a digital fingerprint that solely corresponds to the object and includes the quantitative data set, and generate an identification corresponding to the digital fingerprint, in which the identification includes an alphanumeric string that is displayable as text, graphic, or audio to identify the object, or (ii) authenticate the object by evaluating the quantitative data set produced for the object by comparing with previously-generated quantitative data sets corresponding to other objects in order to determine an authenticity of the object to be the same object as one of the other objects; and one or more data storage devices, in communication with the data processing device, to store the acquired data and analyzed data.
  • a method for creating an identification for an object includes acquiring image data of an object in multiple electromagnetic (EM) spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set that represents specific characteristics of the object associated with the multiple EM spectrums for each sample region; creating a digital fingerprint that includes the quantitative data set, in which the digital fingerprint solely corresponds to the object; generating an identification corresponding to the digital fingerprint, in which the identification includes an alphanumeric string that is displayable as text, graphic, or audio (or any combination thereof) to identify the object; and storing the digital fingerprint and corresponding identification.
  • EM electromagnetic
  • a method for authenticating an object includes acquiring image data of an object in multiple electromagnetic (EM) spectrums at selected sample regions among a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set that represents specific characteristics of the object associated with the multiple EM spectrums for each selected sample region; and evaluating the quantitative data set produced for the object by comparing with previously-generated quantitative data sets associated with other objects stored in a database to determine an authenticity of the object to be the same object as one of the other objects, in which the previously-generated quantitative data sets are each included in a digital fingerprint that solely corresponds to the respective other object, in which the previously-generated quantitative data sets each represents specific characteristics analyzed from images acquired in multiple EM spectrums corresponding to the other objects.
  • EM electromagnetic
  • a method for creating an identification for an object includes acquiring image data of an object in multiple light spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the multiple light spectrums for each sample region; generating a digital identification comprising a unique digital fingerprint, based on the specific characteristics, in which the digital identification solely corresponds to the object; and storing the generated digital identification.
  • the multiple light spectrums include two or more of an infrared (IR) spectrum, a visible light spectrum, an ultraviolet (UV) spectrum, or an X-ray spectrum.
  • the method further includes, or alternatively includes, acquiring hyperspectral image data of the object in at least some of two or more light spectrums along the coordinated array of sample regions; and analyzing the acquired
  • the unique digital fingerprint is based on the specific characteristics of the object derived from the produced quantitative data set including the hyperspectral data.
  • a method for authenticating an object includes acquiring image data of an object in multiple light spectrums at selected sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the multiple light spectrums for each selected sample region; evaluating the quantitative data set produced for the object by comparing with previously-generated digital identifications stored in a database to create a validation score, in which the previously-generated digital identifications each comprise a unique multi- spectral fingerprint based on the specific characteristics analyzed from multi- spectral images of corresponding objects, and in which the validation score is indicative of a sufficiency of data to make a comparison of the quantitative data set with the previously-generated digital identifications; and when the validation score exceeds a predetermined threshold, determining a verdict of authenticity of the object to be authentic, inauthentic, or incomplete as matched to a previously-generated identification.
  • the method includes repeating the acquiring image data to obtain additional
  • a system for identifying and authenticating an object includes a data acquisition platform to acquire data from an object; a data processing device, in communication with the data acquisition platform, to analyze the acquired data of the object and produce a quantitative data set including specific characteristics of the object based on the analyzed data, in which the data processing device is configured to identify the object by generating a digital identification based on the specific characteristics that solely corresponds to the object, or to authenticate the object by evaluating the quantitative data set produced for the object by comparing with previously-generated digital identifications to determine an authenticity; and one or more data storage devices, in communication with the data processing device, to store the acquired data and analyzed data.
  • the data acquisition platform includes a multi- spectral imaging system configured to acquire image data of the object in multiple light spectrums along a coordinated array of sample regions of the object.
  • the system is configured to determine the authenticity by evaluating the quantitative data set produced for the object by comparing with previously-generated digital identifications stored in the one or more data storage devices to create a validation score, in which the previously-generated digital identifications each comprise a unique multi- spectral fingerprint based on the specific characteristics analyzed from multi- spectral images of corresponding objects, and in which the validation score is indicative of a sufficiency of data to make a comparison of the quantitative data set with the previously- generated digital identifications, and when the validation score is determined to exceed a predetermined threshold, determining a verdict of authenticity of the object to be authentic, inauthentic, or incomplete as matched to a previously-generated identification, and when the validation score is determined to not exceed the predetermined threshold, repeating acquisition of image data to obtain additional multi- spectral image data.
  • FIG. 1A shows a diagram of an example embodiment of multi-modal art
  • FIG. IB shows a functional block diagram depicting operations of the example system shown in FIG. 1A.
  • FIG. 1C shows a diagram of an example embodiment of a method for generating a unique identification that solely corresponds to an object.
  • FIG. ID shows a diagram of an example embodiment of a digital fingerprint associated with an object in accordance with the present technology.
  • FIG. IE shows a diagram of an example embodiment of a method authenticating an object based on existing unique identifications.
  • FIG. 2A shows a diagram of an example embodiment of an art identification and authentication imaging system in accordance with the present technology.
  • FIG. 2B shows an image of an example embodiment of a multi- spectral imaging platform in accordance with the present technology.
  • FIG. 2C shows a diagram depicting an example array of sample regions of an artistic work from which multi- spectral image data is acquired and analyzed by the art identification and authentication system in accordance with the present technology.
  • FIG. 2D shows an illustrative diagram of an example smartphone device displaying a user interface of a software application in accordance with the present technology.
  • FIG. 3 shows a diagram of an example embodiment of a device for implementing art identification and/or authentication techniques in accordance with the present technology.
  • the assigned value of an art work can fluctuate radically and suddenly, which may be due to external influences of collectors, dealers, curators, critics, connoisseurs or other.
  • the influences and assessments of these actors may often outweigh other aspects including historic, intellectual, and aesthetic values associated with the artistic work.
  • the disclosed diagnostic characterization techniques include multi- spectral imaging methods that acquire images of an object, such as an artistic work, in multiple, distinct frequency bands of the electromagnetic (EM) spectrum, e.g., corresponding to the frequency bands of the infrared (IR) spectrum, the visible light spectrum, the ultraviolet (UV) spectrum, and/or the X-ray spectrum. Additionally or alternatively, the disclosed diagnostic characterization techniques include hyperspectral imaging methods that acquire images of the object in a plurality of narrow frequency bands spanning a wider band in multiple regions of the electromagnetic spectrum.
  • EM electromagnetic
  • IR infrared
  • UV ultraviolet
  • the disclosed diagnostic characterization techniques can acquire multi- spectral images that include multiple images at a certain point or region of the object (e.g., aimed at a coordinate), where each image is captured within a particular frequency band of an EM spectral group for two or more EM spectral groups.
  • multi- spectral imaging of the certain point or region of the object could include acquiring (1) an image within the IR spectrum (near IR (NIR), short-wavelength IR (SWIR), mid-wavelength IR (MWIR), long- wavelength IR (LWIR), far IR (FIR), or a combination thereof), (2) an image within the visible light spectrum, (3) an image within the UV spectrum (e.g., near UV, middle UV, extreme UV, or a combination thereof), and (4) an image within the X-ray spectrum (e.g., soft X-ray, hard X-ray, or both), or any combination thereof that includes at least two EM spectral images.
  • NIR near IR
  • SWIR short-wavelength IR
  • MWIR mid-wavelength IR
  • LWIR long- wavelength IR
  • FIR far IR
  • the acquired images in the multiple EM spectra for the certain point or region of the object are processed to form a multi- spectral image of the object at that certain point or region.
  • the multi- spectral image acquisition includes acquiring additional multi- spectral images at additional points or regions of the object.
  • the disclosed diagnostic characterization techniques can implement hyperspectral imaging in addition or alternatively to multi- spectral imaging.
  • the disclosed diagnostic characterization techniques can acquire hyperspectral images that include multiple images at a certain point or region of the object (e.g., aimed at a coordinate), where each image is captured within one or more narrow frequency bands within an EM spectral group or groups.
  • hyperspectral imaging of the certain point or region of the object could include acquiring (1) an image at 1.1 ⁇ and/or 1.7 ⁇ within the IR spectrum, and/or (2) an image at 440 to 465 nm (an example narrow blue light band) and/or 510 to 535 nm (an example narrow green light band) within the visible light spectrum.
  • hyperspectral imaging of the certain point or region of the object could include acquiring (1) an image at 370 nm, 340 nm, and/or 300 nm within the UV spectrum, and/or (2) at 1.1 ⁇ and/or 2.4 ⁇ within the IR spectrum.
  • hyperspectral imaging of the certain point or region of the object could include acquiring (1) an image at 370 nm, 340 nm, and/or 300 nm within the UV spectrum, and/or (2) an image at 0.7 nm and/or 1 nm within the X-ray spectrum.
  • additional narrow-band images in other EM spectral groups at the certain point or region of the object can be included.
  • the acquired images within the plurality of narrow bands across multiple EM spectral groups for the certain point or region of the object are processed to form a hyperspectral image of the object at that certain point or region.
  • the hyperspectral image acquisition includes acquiring additional hyperspectral images at additional points or regions of the object.
  • a unique digital identification of an artistic work referred to as a "fingerprint” or “digital fingerprint,” is produced based on multi- spectral and/or hyperspectral data for use in identification and/or authentication of the artistic work.
  • the fingerprint also referred to as a unique multiple EM spectral fingerprint, includes a time series of one or more data blocks each having (1) a quantitative data set of the processed multi- spectral and/or hyperspectral imaging data that was acquired at a particular time for the specific object (e.g., artistic work, historic artifact, collectible, etc.), in which the fingerprint has a digital identification (ID) associated to it for referencing the object to its unique multiple EM spectral fingerprint, where the ID can be an alphanumeric string representable as text ID, graphic ID, and/or audio ID).
  • ID digital identification
  • the fingerprint can include a first data block associated with a first quantitative data set of processed multi- spectral and/or hyperspectral imaging data acquired from the object at a first point in time, a second data block associated with a second quantitative data set of processed multi- spectral and/or hyperspectral imaging data acquired from the object at a second, subsequent point in time, etc.
  • a fingerprint includes at least one data block.
  • the fingerprint can organize the data blocks in any particular order, e.g., as part of data security protocol.
  • the fingerprint includes a data set that is completely unique to a particular piece of art, in which, in some implementations, the fingerprint is created by multi- spectral and/or hyperspectral imaging and advanced image processing techniques.
  • the multi- spectral and/or hyperspectral imaging is noninvasive in acquiring spectral data across many physical layers of the art piece (e.g., a range of depth from the surface of the artistic work) and in many domains (e.g., frequencies or wavelengths).
  • the acquired multi- spectral and/or hyperspectral data is processed using the analytical algorithms to produce the fingerprint, which the raw and processed image data can be stored in a proprietary database.
  • some of the data of a fingerprint may be encrypted for data security such that it can be distributed across a network of computers (e.g., the Internet), in a manner similar to blockchain.
  • the multi- spectral imaging techniques do not require expensive data acquisition technology or equipment, and do not require a laboratory for data acquisition from the artistic work.
  • the artistic work can include, but are not limited to, paintings, sculptures, drawings, photography, printed material, or other art work.
  • FIG. 1A shows a diagram of an example embodiment of a multi-modal art identification and/or authentication system 100 in accordance with the present technology.
  • the system 100 includes a data acquisition platform 102 configured to acquire data from an artistic work 101 and/or obtain information pertaining to the artistic work 101.
  • the system 100 includes a data processing device 104, in communication with the data acquisition platform 102, to process the acquired data of the artistic work 101 for analysis, storage, and/or display of the acquired data or obtained information.
  • the system 100 includes one or more data storage devices 106, in communication with the data processing device 104, to store and maintain the acquired data or obtained information and analyzed data from the data processing device 104.
  • the system 100 includes an output device 108, in communication with the data processing device 104, to delivery and/or present the acquired, analyzed and/or obtained data or information to a user operating the output device or to another device in communication with the device 108.
  • the data acquisition platform 102 includes one or more data acquisition devices to acquire image data using one or multiple modalities, such as imaging (e.g., uni-spectral, multi-spectral and/or hyper spectral), chemical sampling (e.g., invasive and noninvasive), and/or monitoring (e.g., continuous or intermittent monitoring of various conditions such as temperature, humidity, light exposure, location, etc.).
  • imaging e.g., uni-spectral, multi-spectral and/or hyper spectral
  • chemical sampling e.g., invasive and noninvasive
  • monitoring e.g., continuous or intermittent monitoring of various conditions such as temperature, humidity, light exposure, location, etc.
  • images can be acquired using a robotic motion platform that precisely positions an imager, such as a camera, to detect light waves from select regions of the artistic work 101 in a coordinated movement.
  • the imager can be configured as to acquire images from the infrared (IR) spectrum, visible light spectrum, ultraviolet (UV) spectrum, and/or X-ray spectrum.
  • Chemical sampling can include noninvasive, non-destructive techniques, such as various Terahertz or X-ray spectroscopy.
  • Monitoring data can be provided to the data processing device 104 by the monitoring device of the data acquisition platform 102 in real-time or post hoc.
  • the data acquisition platform 102 can include one or more imaging devices to obtain images in one or more spectra for image analysis; whereas, additionally or alternatively, the data acquisition platform 102 can include one or more chemical sampling devices to obtain a physical sample from the artistic work 101 for chemical analysis of the artistic work 101.
  • the data acquisition platform 102 can include other electromagnetic, optical and/or sound based sensing systems, including radar, laser, and ultrasound imaging systems.
  • the data acquisition platform 102 is configured to obtain information about the artistic work.
  • the data acquisition platform 102 can include one or more computing devices that perform and/or obtain research directly about or relevant to the artistic work 101, such as the artistic work's provenance, history (including ownership, handling, modifications, restorations, etc.), legal documentation, or other.
  • the data acquisition platform 102 can be configured to access 3rd party databases and resources and collect large amounts of information known related to the artistic work 101, such as information about other paintings and artists, e.g., which the system 100 can store and process to compare against in analysis of the artistic work 101.
  • the data processing device 104 is embodied one or more computing devices such as a computer (e.g., server, desktop, or laptop) or smart device (e.g., smartphone, tablet, or wearable computing device) that includes a processor to process the data, a memory in communication with the processor to store data, and an input/output unit (I/O) to interface the processor and/or memory to other modules, units or devices of the system 100, and/or external devices.
  • the processor can include a central processing unit (CPU) or a microcontroller unit (MCU).
  • the memory can include and store processor-executable code, which when executed by the processor, configures the data processing device 104 to perform various operations, e.g., such as receiving information, commands, and/or data, processing information and data, and transmitting or providing information/data to another device.
  • the data processing device 104 can include a computer system or communication network accessible via the Internet (referred to as 'the cloud') that includes one or more remote computational processing devices (e.g., servers in the cloud).
  • the data processing device 104 can include a local computer that is in communication with the data acquisition device(s) 102 and include one or more cloud computers and computational devices that are communication with the local computer.
  • the data processing device 104 includes computers configured in the cloud, in clusters, and/or in neural networks.
  • the memory can store information and data, such as instructions, software, values, images, and other data processed or referenced by the processor.
  • information and data such as instructions, software, values, images, and other data processed or referenced by the processor.
  • RAM Random Access Memory
  • ROM Read Only Memory
  • Flash Memory Flash Memory
  • I/O of the data processing device 104 can interface the data processing device 104 with a wireless
  • the I/O of the data processing device 104 can also interface with other external interfaces, sources of data storage, and/or visual or audio display devices, etc. to retrieve and transfer data and information that can be processed by the processor, stored in the memory, or exhibited on the output device 108 or other external device.
  • a display unit of the output device 108 can be configured to be in data communication with the data processing device 104, e.g., via the I/O, to provide a visual display, an audio display, and/or other sensory display that produces a user interface of a software application ("app") in accordance with the art identification and/or authentication techniques of the disclosed technology.
  • the display unit can include various types of screen displays, speakers, or printing interfaces, e.g., including but not limited to, light emitting diode (LED), or liquid crystal display (LCD) monitor or screen, cathode ray tube (CRT), and/or hologram as a visual display; audio signal transducer apparatuses as an audio display; and/or toner, liquid inkjet, solid ink, dye sublimation, inkless (e.g., such as thermal or UV) printing apparatuses, etc.
  • LED light emitting diode
  • LCD liquid crystal display
  • CRT cathode ray tube
  • hologram as a visual display
  • audio signal transducer apparatuses as an audio display
  • toner liquid inkjet, solid ink, dye sublimation, inkless (e.g., such as thermal or UV) printing apparatuses, etc.
  • the data storage device(s) 106 includes one or more computing devices operating a database management system (DBMS) to maintain and control operations of various databases of the system 100.
  • DBMS database management system
  • the system 100 can include an identification database to store and organize all data acquired and obtained for a particular artistic work 101 assigned a unique identification based on the analyzed characteristics, analyzed by the data processing device 104, of the artistic work 101.
  • the data storage device 106 can include a cultural database to store and organize research information, such as the artistic work's provenance, history, etc.
  • the data storage device 106 can include an authentication database to store and organize information pertaining authentication events of the artistic work 101, discussed later in this disclosure.
  • the data storage device 106 can include a transactions database to store and organize information and/or legal documents pertaining to financial transactions or offers associated with the artistic work 101.
  • These example databases can be included as a singular database or as individual databases.
  • 3rd party databases can be used to store and organize information pertaining to the artistic work 101, e.g., such as the cultural database or blockchain registries.
  • the output device 108 includes a personal computer such as a desktop or laptop computer, a mobile computing device such as a smartphone, tablet, smartwatch, etc., or other computing device to display data from the data processing device 104, the data acquisition platform 102, and/or the data storage device 106.
  • the output device 108 includes a software application (“app") that is stored on the output device 108 and controls various functionalities associated with the system 100 such as processing, storing and displaying data received from the data processing device 104.
  • FIG. IB shows a functional block diagram depicting operations of the system 100.
  • the system 100 is configured to gather data and information, at 110; to process the data and information, at 120; to store and organize the processed data and
  • the system 100 can perform certain operations among the data gathering operations 110, the data processing operations 120, the data storage operations 130 and the data delivery operations 140 based on a particular application or applications, at 150, such as creating an identification of the artistic work 101, authenticating the artistic work 101, diagnosing and/or monitoring the artistic work 101, and/or grouping or discovering relational features from the artistic work 101 with other similar objects.
  • the system 100 can gather data including acquiring image data, procuring chemical samples for chemical analysis, monitoring conditions of the artistic work 101, and/or obtaining data directly about or indirectly relevant to the artistic work 101.
  • the data processing device 104 can perform data processing operations 120 on the gathered data, including but not limited to the following.
  • the data processing device 104 can generate a unique identification (e.g., a digital fingerprint) that solely corresponds to the artistic work 101 based on the physical characteristics of the artistic work determined from the analysis of the gathered data and information.
  • the data processing device 104 can authenticate the artistic work 101 based on a multi-stage technique that validates the unique identification of the artistic work with analyzed characteristics from a current data acquisition session of the artistic work 101. Such authentication implementations can determine authenticity of the artistic work, inauthenticity of the artistic work, or an incomplete authentication session.
  • the data processing device 104 can further authenticate the artistic work 101 for subsequent diagnostic and monitoring purposes (e.g., for a scientific condition report/clinical chart), which can be used to assign and/or update an assigned 'value' to the artistic work 101, such as a material degradation, no contemporary modifications to the work, or outright damage.
  • the data processing device 104 can further analyze the artistic work 101 with data associated with other artistic works or objects to create groupings of the artistic work 101.
  • the system 100 can perform machine-learning to determine such groupings. By querying the archive of previously studied works and the data associated with those studies, they can be compared to the data associated with artistic work 101 to validate or invalidate consistencies.
  • the system 100 e.g., implementing the example data processing device 104, can receive and process gathered data, in which the received data and the processed data can be stored by the data storage device 106.
  • the data storage device 106 can perform data storing and organizing operations 130 on data, including but not limited to the following.
  • the data storage device 106 can store and retrieve data in the authentication database, including the digital fingerprint for the analyzed artistic work 101, raw images or pre-preprocessed obtained data acquired by the data acquisition platform 102, processed data (e.g., images or chemical samples) with metadata associated with the data acquisition and/or processing, proprietary metadata (e.g., topography, color, age, state of health/conservation/degradation, pigment specifics, etc.), images and monitoring data acquired over time (e.g., continuous or time slices with changes), and references and information to known fakes and similar items.
  • processed data e.g., images or chemical samples
  • proprietary metadata e.g., topography, color, age, state of health/conservation/degradation, pigment specifics, etc.
  • images and monitoring data acquired over time e.g., continuous or time slices with changes
  • references and information to known fakes and similar items e.g., continuous or time slices with changes.
  • the data storage device 106 can store and retrieve data in the cultural database, such as provenance information, historical information, artist information, context, images and the like, each of which can include metadata; the data storage device 106 can store and retrieve data in the authentication database via the digital identification, such as an authentication record of past authentication sessions performed on the artistic work 101 and analytical data associated with such authentication sessions; and the data storage device 106 can store and retrieve data in the transactions database.
  • the cultural database such as provenance information, historical information, artist information, context, images and the like, each of which can include metadata
  • the data storage device 106 can store and retrieve data in the authentication database via the digital identification, such as an authentication record of past authentication sessions performed on the artistic work 101 and analytical data associated with such authentication sessions
  • the data storage device 106 can store and retrieve data in the transactions database.
  • the system 100 is operable to produce an identified work data structure that includes a log of activities of the artistic work 101, a log of inquiries about the artistic work 101, and/or a log of transactions (e.g., financial transactions including loans, change of ownership, insurance) of the artistic work.
  • the data structure can include a chain of events recorded in plain language text and/or images associated with the transactions, activities or inquires, in which the chain of information is represented in the data structure as code.
  • the data structure is encrypted.
  • the system 100 can receive, transfer, and/or present data to perform data delivery operations 140.
  • Examples of displayable data by the output device 108 include multi-level image data display, which can show multiple layers of analyzed data from the artistic work 101 and/or multiple spectral levels analyzed from an image or sample of the artistic work 101.
  • Examples of displayable data include confirmation or denial of validation from an authentication session, that is whether or not the artistic work 101 is authenticated based on the digital identification.
  • Examples of displayable data include condition reports, e.g., which can provide information about changes made to the artistic work over time.
  • Examples of displayable data include a digital audit trail, e.g., including provenance, transactional history (e.g., sale, insurance, claims, changes/restorations), or block chain information.
  • Examples of displayable data include alerts, e.g., including potential fraud alerts, such as someone trying to obtain the digital identification for an object in the user's possession.
  • the displayable data can include condition alerts, such as an environmental condition change that would affect the artistic work, e.g., including an increase or decrease in humidity and/or an increase or decrease in temperature, and/or an unexpected light illumination instance upon the artistic work, or an increase or decrease from the desired light illumination on the artistic work.
  • alerts include a 'similar for sale' alert, or a 'buyer interested' alert.
  • displayable data include information about the artistic work 101, such as the context information, the authentication information, and information about comparable works (e.g., such as pricing, history and location of a comparable work of art).
  • alerts originating from the system can be sent to the owner of the ID, e.g., such as a fraud alert, or as an inquiry alert based on inquiries associated.
  • statistic information about inquiries of similar works e.g., artist, period value
  • FIG. 1C shows a diagram of an example method 160 to generate a unique
  • the method 160 includes a process 162 to acquire image data of an object in multiple electromagnetic (EM) spectrums, e.g., using multi- spectral imaging and/or hyperspectral imaging.
  • the method 160 includes a process 164 to produce a quantitative data set associated with the multiple EM spectral images.
  • the method 160 includes a process 165 to create a digital fingerprint that includes the quantitative data set, in which the digital fingerprint solely corresponds to the object.
  • implementations of the processes 164 and 165 can produce a fingerprint comprising data that can only be linked to the object, and none other, based on the unique physical characteristics of the object manifested in the quantitative data.
  • the method 160 includes a process 166 to generate an identification that corresponds to the digital fingerprint (e.g., unique multiple EM spectral quantitative data set of the fingerprint), where the identification only references the object.
  • the method 160 includes a process 168 to store the generated identification and the acquired data (e.g., the acquired image data and metadata) and processed data (e.g., the produced quantitative data set, metadata, and data artifacts recorded by a user or from the processed image data) in a database.
  • the data artifacts can include scientific points of interest associated with distinguishing features of the object, which can be included in the stored data by user input of the data artifact or by automated detection of a distinguishing feature of the object from the quantitative data set.
  • a data artifact could include craquelure (i.e., an age-related/drying feature common in oil painting that appears as cracking on the surface), an under-drawing (i.e., a preparatory drawing often in graphite or charcoal), a non-contemporaneous restoration (e.g., often seen as paint loss that has been covered up) or pentimenti (i.e., alterations made by the artist as they create the work)
  • the process 162 can include acquiring the image data by capturing multi- spectral images for at least two points or regions of the object, where each multi- spectral image associated with a point or region is captured within a particular frequency band of an EM spectral group for two or more EM spectral groups.
  • the process 162 can include acquiring the image data by capturing hyperspectral images for at least two points or regions of the object, where each hyperspectral image associated with a point or region is captured within one or more narrow frequency bands within an EM spectral group or groups.
  • the process 162 can include acquiring the image data by capturing multi- spectral and hyperspectral images of the object.
  • the quantitative data set produced in the process 164 represents physical
  • the processes 164 and 166 can include the data processing device 104 creating a pixel by pixel dataset/point cloud (set of data points in space) of each layer imaged by the data acquisition platform 102, e.g., after the imaging is completed or concurrently with the imaging.
  • each pixel, in each layer can contain more than five unique attributes. The combinations created by so many unique pixels across multiple layers can be in the billions, for example.
  • the data processing device 104 can encode and decode these unique pixel attributes to secure the information that is undiscoverable by a third party.
  • This data is encrypted so that only the data processing device 104 and computing devices authorized by the data processing device 104 can decode it.
  • the processes 164 and 166 include producing an encryption algorithm to unscramble all potential incorrect combinations to the one and only true original authenticated combination (e.g., the generated identification, i.e., digital fingerprint) unique to that specific artistic work.
  • the encryption algorithm can include backwardation of a Gaussian diffusion of the data, in which the algorithm determines the possible paths of the data and identifies a positive identification.
  • the system 100 can statistically reconcile the slight variability in acquired image data in implementations of the encryption algorithm.
  • the encryption algorithm can use a mathematical Gaussian diffusion function, in which the algorithm determines the possible paths of all combinations of data, and will respect variances between data points as long as the cumulative result still falls into the normal distribution, and therefore identifies a positive identification with statistical surety.
  • FIG. ID shows a diagram of an example embodiment of a digital fingerprint associated with an object in accordance with the present technology, labeled as fingerprint 1000.
  • the digital fingerprint 1000 includes one or more data blocks that contain an organized set of data for a particular object (e.g., artistic work).
  • the digital fingerprint 1000 includes two data blocks (i.e., data block 0 and data block 1), but may include any number of data blocks.
  • the digital fingerprint 1000 includes at least one data block, i.e., data block 0.
  • Each data block of the digital fingerprint 1000 is associated with a multi- spectral and/or hyperspectral imaging event of the object at a point in time, e.g., the processed multi- spectral imaging data, hyperspectral imaging data, or any combination of both, in accordance with the process 162.
  • the multi- spectral and/or hyperspectral data in the digital fingerprint can include digital processed image data or the acquired (raw) imaging data associated with the particular locations (coordinates) of the object that are imaged by multi- spectral and/or hyperspectral imaging.
  • Each data block of the digital fingerprint 1000 can include metadata, e.g., (i) imaging metadata such as a date/time of imaging, imaging settings, etc.
  • proprietary metadata such as information about the object like author/artist name, country of origin, provenance info, topography info, object materials and attributes info (such as colors of paint, type of paint, etc.), age, pigment specifics, past and present state of
  • a data block can be included that is not associated with a multi- spectral and/or hyperspectral imaging session, e.g., such as a data block that may just include information about the object, such as a recent change in its condition.
  • the data block 0 includes multi- spectral and/or hyperspectral image data associated with a first imaging session at a first time to, e.g., in accordance with the method 160.
  • the multi- spectral and/or hyperspectral image data includes images associated with selected spectral bands at particular coordinates of the object (x,y,z).
  • the multi- spectral and/or hyperspectral image data includes a data set for all coordinates of the object (e.g., xo,yo,zo to x n ,yn,z n ) for image spectra 1, image spectra 2, ... image spectra m.
  • the image spectra data set for image spectra 1 may correspond to the set of processed image data for each coordinate of the object, where each image is acquired in a particular EM spectral band in accordance with a multi- spectral imaging protocol; or the image spectra data set for image spectra 1 may correspond to the set of processed image data for each coordinate of the object, where each image is acquired in a narrow frequency band of a particular EM spectral band in accordance with a hyperspectral imaging protocol.
  • FIG. ID shows the image data set for all coordinates of the object, in some implementations the data block 0 is not required to have multi- spectral and/or hyperspectral image data for every coordinate.
  • the example shown in FIG. ID depicts the digital fingerprint 1000 including the data block 0 and a data block 1.
  • the multi- spectral and/or hyperspectral data of data block 1 includes the processed image data associated with selected spectral bands at particular coordinates of the object (x,y,z), which in this example, is a smaller subset than the number of coordinates in data block 0.
  • the image spectra data set of data block 0 includes the multi- spectral and/or hyperspectral image data for the same spectra 1, spectra 2 ... etc. as in the data block 0.
  • the data blocks of the digital fingerprint 1000 can have image data sets that may vary in image spectra selected and/or object coordinates imaged with respect to other data blocks in the data block chain.
  • the multi- spectral and/or hyperspectral data include the processed multi- spectral and/or hyperspectral data, which includes a quantitative data set representing the specific physical characteristics of the object at the point or region of the object that was imaged by the multi- spectral and/or hyperspectral imaging technique.
  • the raw imaging data is stored in the one or more data storage devices 106 and managed by the database management system (DBMS), and the processed imaging data is recorded (which can be encrypted) in the fingerprint 1000, in which the fingerprint 1000 can be stored the one or more data storage devices 106).
  • the raw imaging data may also be included in the fingerprint 1000.
  • the fingerprint 1000 (e.g., the data block chain) is stored in a proprietary database.
  • some or all of the data in the data blocks of the fingerprint 1000 may be also stored in encrypted form on the data block chain, in which the limited and encrypted version of the fingerprint can be disseminated across a network of computers (e.g., the Internet).
  • a network of computers e.g., the Internet.
  • peer-to-peer data sharing requires the use of web services to communicate, and thus the proprietary data of the fingerprint is never fully transmitted to the client node, but instructions and/or results associated with the digital identification of an object may be permitted to transfer in such manner.
  • Implementations of the method 160 can include one or more of the following features.
  • the process 162 can include acquiring chemical data from the object using pseudo-invasive or noninvasive chemical sampling techniques.
  • a noninvasive chemical sampling technique includes Terahertz or X-ray spectroscopy
  • a pseudo-invasive chemical sampling technique includes SEM-EDS analysis of cross-section samples (e.g., biopsies of the art work) or Raman Spectroscopy.
  • FIG. IE shows a diagram of an example method 170 to authenticate an object using the existing unique identification assigned to the authentic object (e.g., artistic work 101), from the method 160, based on data gathering operations 110 and data processing operations 120 in compilation with data storage operations 130.
  • the method 170 includes a process 172 to acquire image data in multiple electromagnetic (EM) spectrums along a directed array of sample regions of the object.
  • the method 170 includes a process 174 to produce a quantitative data set associated with the multi-EM spectral images along the array.
  • the produced quantitative data set can include a pixel by pixel data set across a portion or all of the imaged layers, e.g., containing unique attributes about each individual pixel across multiple layers of the object.
  • implementations of the processes 172 and 174 can include producing a subsequent data block in the digital fingerprint 1000, e.g., such as the example data block 1, where only a portion of the object was imaged and its imaging data processed to produce the quantitative data set associated with the multi- spectral and/or hyperspectral images.
  • the method 170 includes a process 176 to analyze the quantitative data set produced for the imaged object to compare with previously generated quantitative data sets (e.g., fingerprints of other objects, which have their respective identifications), which are stored in the database (i.e., storing existing unique multi-EM spectral fingerprints) to determine an authenticity of the object to be the same object as one of the other objects.
  • previously generated quantitative data sets e.g., fingerprints of other objects, which have their respective identifications
  • the database i.e., storing existing unique multi-EM spectral fingerprints
  • the process 176 includes creating a validation score indicative of sufficiency of data to the object.
  • the validation score is sufficient, i.e., indicating sufficient data to make a comparison between the quantitative data set and the data associated with existing multi- EM spectral fingerprints
  • the method 170 includes a process 178 to determine a verdict of authenticity of the object to be authentic, inauthentic, or incomplete as matched to a valid, existing identification.
  • the method 170 may include repeating the process 172 to obtain the image data along the directional array and/or obtain additional data, e.g., a different directional array of the object.
  • FIG. 2A shows a diagram of an example embodiment of an art identification and authentication multi- spectral and/or hyperspectral imaging system 200 in accordance with the present technology.
  • the system 200 includes a multi- spectral imaging platform 210 configured to acquire image data in multiple light spectrums of an artistic work 201 along a coordinated array of sample regions of the artistic work 201.
  • the multi- spectral imaging platform 210 can acquire image data in one or more of the infrared (IR) spectrum, visible light spectrum, ultraviolet (UV) spectrum, and/or X-ray spectrum.
  • IR infrared
  • UV ultraviolet
  • the multi- spectral imaging platform 210 includes a robotic motion positioning system coupled to one or more imagers to precisely coordinate movement and position the imager(s) to detect light waves from select regions of the artistic work 201 and capture such images in one or more spectra, e.g., IR, visible light, UV, and/or X-ray.
  • a robotic motion positioning system coupled to one or more imagers to precisely coordinate movement and position the imager(s) to detect light waves from select regions of the artistic work 201 and capture such images in one or more spectra, e.g., IR, visible light, UV, and/or X-ray.
  • the multi- spectral imaging platform 210 can record one or more of the following: IR image data using IR reflectography (IRR) and/or IR photography (IRP) imaging techniques; UV image data using UV fluorescence imaging techniques; visible light image data using photomicrography imaging techniques; and/or X-ray image data using X-ray imaging techniques.
  • IRR IR reflectography
  • IRP IR photography
  • UV image data using UV fluorescence imaging techniques
  • visible light image data using photomicrography imaging techniques and/or X-ray image data using X-ray imaging techniques.
  • the multi- spectral imaging platform 210 can obtain metadata associated with each scan, e.g., including but not limited to date, time, location, temperature, humidity, and other data associated with the data acquisition process and environment of the artistic work being scanned.
  • FIG. 2B shows an image of an example embodiment of the multi- spectral imaging platform 210.
  • the example imaging platform shown in FIG. 2B includes imaging sensors designed to collect data on the subject art work while recording the data in precise geometry (x,y,z) coordinates of every data point.
  • Example imaging sensors can include one or more cameras that can capture multi- spectral images, hyperspectral images, or a combination thereof.
  • the one or more cameras can include an SLR camera with filter lenses that filter out the undesired light wavelengths such that only the desired EM spectral frequency range or ranges of light are captured by the camera.
  • the image sensors to obtain multi- spectral and/or hyperspectral data can include any of: a Nikon D810 or D7000 (e.g., with or without internal IR filter removed), Nikon D7000 (e.g., with or without internal IR filter removed), Phase One IQ4, Machine Vision Camera (e.g., Keyence - XG-X
  • 2D/3D Laser Scanner e.g., LJ-V7000
  • SWIR Camera e.g., Widy SWIR 320, or Xenics Bobcat -640-CL, or Xenics Tigris-640
  • the image sensors are attached to a robotic motion positioning system, secured to a surface (e.g., table) on which the artistic work 201 sits, to precisely coordinate movement and position the imager(s) to detect the select EM waves from select regions of the artistic work 201.
  • Control software directs the data collection including precise lighting, light bandwidth (through lens filter combinations) as well as exposure parameters (aperture, duration, focus) for every image.
  • the multi- spectral imaging platform 210 includes a lighting bracket that travels with the camera to ensure the same lighting conditions for each image acquired in the array of sample regions.
  • FIG. 2C shows a diagram depicting an example array of sample regions 205 of the artistic work 201 of which the multi- spectral imaging platform 210 obtains the multi- spectral image data from each sample region of the array 205.
  • the array of sample regions 205 is configured over a select portion of the artistic work 201, e.g., such as some or all of a surface of the artistic work 201.
  • the array of sample regions 205 includes a contiguous array of sample regions across the painted surface of the painting, and through various depths beneath the surface of the painting (which can be defined as layers).
  • the array of sample regions 205 can have x, y, z coordinates defining an area and a volume of a sample of the array 205.
  • the sample regions of the array 205 are contiguous across one dimension, two dimensions, or three dimensions of one or more surfaces of the artistic work 201; whereas in other implementations, the sample regions of the array 205 are discontiguous in at least some portions of the artistic work 201.
  • the array of sample regions 205 is configured to include a frame of reference point or points associated with certain location(s) on the artist work 201 for defining relative distances between the sample regions of the array 205.
  • the system 200 includes a data processing device 220 configured to process the multi- spectral image data of the artistic work 201 acquired by the multi- spectral imaging platform 210.
  • Example embodiments of the data processing device 220 can include those of the data processing device 120.
  • the data processing device 220 is configured to process the multi- spectral image data for the sample regions of the array 205 to produce a quantitative data set associated with each sample region.
  • the data processing device 220 is operable to produce an identification, e.g., the fingerprint 1000, of the artistic work 201 and the data processing device 220 is operable to authenticate the artistic work 201, in which either or both operations can be performed in an implementation of the system 200.
  • the system 200 is configured to identify an artistic work and/or authenticate the artistic work by implementing the method 160 and/or the method 170 as shown in FIGS. 1C and IE, respectively.
  • the system 200 includes a database 230 in communication with the data processing device 220.
  • the database 230 is configured to store acquired multi- spectral and/or hyperspectral image data of objects (e.g., the artistic works) and identification data (e.g., fingerprints) of each respective artistic work, e.g., including multiple instances of the data over time.
  • the database 230 can store additional quantitative data about the artistic works, e.g., including quantitative information such as monitoring data associated with the 'health' of the artistic work over time (e.g., temperature obtained from a temperature sensor, light exposure obtained from an optical sensor, humidity obtained from a moisture sensor, etc.); previous authentication checks; or other.
  • the database 230 can store additional qualitative data about the artistic works, e.g., including attribution data about the art work and its artist, provenance data such as information about the origin, prior owners, handling, etc. of the artistic work, and/or history data such as catalogue data (e.g., prior non-quantitative assessments or authentications, such as those performed by an art expert).
  • additional qualitative data about the artistic works e.g., including attribution data about the art work and its artist, provenance data such as information about the origin, prior owners, handling, etc. of the artistic work, and/or history data such as catalogue data (e.g., prior non-quantitative assessments or authentications, such as those performed by an art expert).
  • the database 230 can include one or more sub-databases assigned to certain types of data to be stored and organized for the system 200.
  • the database 230 can include the identification database to store and organize all data acquired and obtained for the artistic work 201 based on implementations of the method 160.
  • the database 230 can include the authentication database to store and organize information pertaining authentication events of the artistic work 201 based on implementations of the method 170.
  • the database 230 can include a cultural database or be in communication with a third party cultural database, which stores information of artistic works associated with provenance, history, etc.
  • the information can be stored according to the art work' s generated
  • the database 230 an include the transactions database to store and organize information associated with financial transactions or legal matters for the artistic work 201.
  • the system 200 includes an output device 240, in communication with the data processing device 104, to delivery and/or present the acquired, analyzed and/or obtained data or information to a user operating the output device or to another device in communication with the device 108.
  • Examples of the output device 240 include a computing device with a display, including but not limited to a desktop or laptop computer, a smartphone, a tablet, or a wearable computing device like a smartwatch or smartglasses.
  • FIG. 2D shows an illustrative diagram of an example smartphone device displaying a user interface of a software application (app) of the disclosed system 100 or 200.
  • the smartphone device embodies the output device 240 (or output device 108).
  • the example smartphone device can also embody at least some of the processing modules of the data processing device 220 (or data processing device 104).
  • the app displays a user interface that can feature the fingerprint ("ID") associated with an analyzed artistic work, and an image depicting the artistic work specifically associated with the fingerprint ID.
  • the app can also display a user input box that allows the user to display other images associated with the acquired multi- spectral and/or hyperspectral data.
  • the visible light image data is displayed on the app's user interface.
  • the app can also include zoom functionality and focusing features to view the image data at certain locations with high resolution.
  • the app provides data entry functionality to add notes or other data to the fingerprint associated with the particular artistic work, e.g., add a new data block in the chain of data blocks.
  • FIG. 3 shows a diagram of an example embodiment of a device 300 that embodies at least some of the devices of the system 100 and 200 as shown in FIG. 1A and FIG. 2A, respectively, such as the data processing device 104 or 220.
  • the device 300 is operable to implement art identification and/or authentication techniques in accordance with the present technology, such as the example methods 160 and 170 described in FIGS. 1C and IB, respectively.
  • the device 300 also includes the output device 108 or 240.
  • the device 300 can be embodied as a smartphone, tablet or wearable computing device, a computer, or other computing device including a display screen.
  • the device 300 includes a wireless communications unit 325 to receive data from and/or transmit data to another device.
  • the wireless communications unit 325 includes a wireless transmitter/receiver (Tx/Rx) unit operable to transmit and/or receive data with another device via a wireless communication method, e.g., including, but not limited to, Bluetooth,
  • Bluetooth low energy Zigbee, IEEE 802.11, Wireless Local Area Network (WLAN), Wireless Personal Area Network (WPAN), Wireless Wide Area Network (WW AN), WiMAX, IEEE 802.16 (Worldwide Interoperability for Microwave Access (WiMAX)), 3G/4G/5G/LTE cellular communication methods, and NFC (Near Field Communication).
  • WLAN Wireless Local Area Network
  • WPAN Wireless Personal Area Network
  • WW AN Wireless Wide Area Network
  • WiMAX IEEE 802.16 (Worldwide Interoperability for Microwave Access (WiMAX)), 3G/4G/5G/LTE cellular communication methods, and NFC (Near Field Communication).
  • the system 100 can include an apparatus set-up in a controlled lab setting and configured specifically for the purpose of imaging and/or
  • An example can include the multi- spectral imaging platform 210, shown in FIG. 2B, in communication with a computer workstation and a database, e.g., in the cloud.
  • the system 100 can include a portable apparatus moveable to an implementation site, e.g., a 'mobile lab', which can include the same or similar capabilities of the example controlled lab setting with respects to some aspects in lighting and imaging, and with the additional capability that it can be delivered anywhere in the world to identify and/or authenticate the artistic work while providing the closest approximation to the full lab.
  • a portable apparatus moveable to an implementation site e.g., a 'mobile lab'
  • an implementation site e.g., a 'mobile lab'
  • the system 100 can include one or more peripheral devices that can be attached to smart device technology (e.g., such as a smartphone or tablet) and used to capture a subset of data that can be used to verify the identity of the work of art, but, the peripheral device(s) may not be able to be used to collect all initial data necessary to create the ID.
  • the system 100 can include the device 300 embodied on a smartphone or tablet, where the data acquisition platform 102 includes the camera of the smartphone or tablet coupled with the peripheral device to provide the capability of acquiring the multi- spectral and/or hyperspectral images in multiple spectra of light from the target object (e.g., artistic work).
  • the system 100 can include a fully integrated hyperspectral or multi- spectral camera or cameras and/or components on a mobile computing device capable of analyzing and determining the identity of a work of art.
  • the system 100 can include the device 300 embodied on a smartphone or tablet.
  • the fully-integrated hyperspectral or multi- spectral camera(s) may not be able to be used to collect all initial data necessary to create the ID.
  • Example Use Case 1 - Stolen Art Identification The disclosed systems and methods can be used to identify art that was stolen during time of war (e.g., 2020 Vietnamese stolen works being the most infamous) or by organized crime or by thieves, and subsequently sold in the black/secondary market. For example, when a work is questionable that it may have been stolen, the works can be compared using the disclosed systems and methods with previous high- resolution images of said work of art. While identification of this work would likely be lower in statistical accuracy, the multi-EM spectral data produced by implementations of the disclosed systems and methods could be used to determine that the work is the same as previously imaged.
  • Example Use Case 2 Auction House Resale -
  • the due diligence typically requires that the work of art be sent to the same auction house location.
  • the artistic work was analyzed by the disclosed systems and methods to produce a digital fingerprint
  • the work of art can be accurately verified prior to being sent to the auction house. This will provide statistical certainty that all previous due diligence can be relied on.
  • the condition of the work can be determined form the digital fingerprint alerting the auction house to any recent defects, damages or changes that impact its value.
  • Example Use Case 3 - Delivery of Art there is no reliable way for a buyer to verify that the artistic work they purchased, e.g., at an auction, is the same artistic work that is delivered to them, unless the buyer or an agent of the buyer is permitted to accompany the artistic work throughout the remaining period post-sale until delivery. Such means are impractical and burdensome, at best.
  • a buyer of a work of art can associate the bill of sale for that specific work of art as secured within a digital fingerprint.
  • the work of art can be analyzed using the disclosed systems and methods to create and/or authenticate the digital fingerprint; and at the point sale, the fingerprint can be updated to include the purchase information.
  • the delivered art work can be again verified by the disclosed systems and methods, e.g., thus avoiding dealers, galleries or other art sellers that unintentionally (or intentionally) try to substitute the work of art for alternates, replicas, copies or fakes.
  • Example Use Case 4 Insurance Company Proof of Life -
  • the digital fingerprint can allow owners or agents of the insurance company to prove the existence and state of health of the art. State of Health or condition can provide a key piece of data for any future damage claims. Protecting the authenticity of the condition report is presently not performed, if not even practical.
  • condition reports can be secured within the digital fingerprint that solely corresponds to the exact artistic work.
  • Example Use Case 5 Secondary Market Sale Verification
  • a secondary market e.g., owner-to-new-owner sale. Tracking and verifying authenticity is nearly impossible in such situations, as secondary market sales lack so-called responsible third-party brokers, such as auction houses.
  • responsible third-party brokers such as auction houses.
  • a condition of any sale of a work of art can require that the item matches the digital fingerprint that is associated with the established digital audit trail of the item. In such situations, even fakes or copies would have their own unique fingerprint, thus distinguishing them from authentic art works created by the original artist.
  • Example Use Case 6 Bank loan Approval -
  • implementations of the disclosed systems and methods can allow for banks to require the verification of the digital fingerprint in order to tie to the digital audit trail of the original work of art. For example, the verification itself and subsequent loan could be recorded as a transaction that would be added to the digital audit trail.
  • the digital fingerprint can include a large set of data collected during one or many authentication studies, which can be organized as subsets of the larger data set. In such cases, addition points or spectra may be used for verification of the artistic work. All art service providers that provide any warrantee (on a work, value added service, sale or other transaction) need some form of verification when present with a claim. Typically, this is done without clear proof that the item is the same that was sold. Implementations of the disclosed systems and methods can be used for verification with the digital fingerprint to limit if not prevent instances of warranty fraud and create surety for warranty holders.
  • VIN vehicle identification number
  • CUSIP CUSIP symbol
  • an artistic work includes a set of several components or pieces, such as a sculpture of a forest having one or more tree sculpture pieces, one or more bush sculpture pieces; a work that is a triptych (e.g., picture or carving on 3 panels); or other multi- component artistic work or object.
  • the disclosed systems and methods can create and authenticate a fingerprint unique to each piece of the set, in which each set piece has its own unique reference ID.
  • the disclosed systems and methods can generate a unifying set ID that can be used to reference the individual reference IDs of the set pieces, so that the unifying set ID can provide a user of the system to obtain data in the individual fingerprints associated uniquely to each set piece.
  • a method for creating an identification for an object includes acquiring image data of an object in multiple (e.g., two or more) light spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the multiple light spectrums for each sample region; generating a digital identification comprising a unique multi- spectral fingerprint, based on the specific characteristics, in which the digital identification solely corresponds to the object; and storing the generated digital identification.
  • Example A2 includes the method of example Al, in which the generated digital identification is stored in a database configured to store other digital identifications that solely correspond to other objects.
  • Example A3 includes the method of example A2, further including storing the acquired image data and processing data comprising data from the produced quantitative data set and processing data artifacts in the database.
  • Example A4 includes the method of example Al, further including acquiring chemical data from the object using an invasive or a noninvasive chemical sampling technique; and analyzing the acquired chemical data to identify additional characteristics of the object; and modifying the quantitative data set to include the additional characteristics.
  • Example A5 includes the method of example Al, in which the multiple light spectrums include any of an infrared (IR) spectrum, a visible light spectrum, an ultraviolet (UV) spectrum, or an X-ray spectrum.
  • IR infrared
  • UV ultraviolet
  • X-ray spectrum any of an infrared (IR) spectrum, a visible light spectrum, an ultraviolet (UV) spectrum, or an X-ray spectrum.
  • Example A6 includes the method of example Al, further including acquiring hyperspectral image data of the object in at least some of the multiple light spectrums along the coordinated array of sample regions; and analyzing the acquired hyperspectral image data to produce hyperspectral data included in the produced quantitative data set.
  • Example A7 includes the method of example Al, in which the acquired image data includes metadata associated with each acquired image.
  • Example A8 includes the method of example A7, in which the metadata includes one or more of a date of image acquisition, a time of the image acquisition, a location of the image acquisition, a temperature around the object, or a humidity around the object.
  • Example A9 includes the method of example Al, in which the coordinated array of sample regions of the object includes a contiguous array of the sample regions across a surface of the object, through a depth of the object, or across a surface and through a depth of the object.
  • Example A 10 includes the method of example Al, in which the coordinated array of sample regions of the object includes a discontiguous array of the sample regions across a surface of the object, through a depth of the object, or across a surface and through a depth of the object.
  • Example Al l includes the method of example Al, in which the acquiring the image data along the coordinate array of sample regions of the object includes using a multidimensional robotic positioning system coupled to an imager.
  • Example A12 includes the method of example Al, in which the object includes an artistic work comprising a sculpture, drawing, photograph, or printed material.
  • a method for authenticating an object includes acquiring image data of an object in multiple light spectrums at selected sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the multiple light spectrums for each selected sample region; evaluating the quantitative data set produced for the object by comparing with previously-generated digital identifications stored in a database to create a validation score, in which the previously-generated digital identifications each comprise a unique multi- spectral fingerprint based on the specific characteristics analyzed from multi- spectral images of corresponding objects, and in which the validation score is indicative of a sufficiency of data to make a comparison of the quantitative data set with the previously- generated digital identifications; and when the validation score exceeds a predetermined threshold, determining a verdict of authenticity of the object to be authentic, inauthentic, or incomplete as matched to a previously-generated identification, and when the validation score does not exceed the predetermined threshold, repeating the acquiring image data to obtain
  • Example A 14 includes the method of example A13, further including storing the acquired image data and processing data comprising data from the produced quantitative data set and processing data artifacts in the database.
  • Example A15 includes the method of example A13, further including acquiring chemical data from the object using an invasive or a noninvasive chemical sampling technique; and analyzing the acquired chemical data to identify additional characteristics of the object; and modifying the quantitative data set to include the additional characteristics.
  • Example A16 includes the method of example A 13, in which the multiple light spectrums include one or more of an infrared (IR) spectrum, a visible light spectrum, an ultraviolet (UV) spectrum, or an X-ray spectrum.
  • IR infrared
  • UV ultraviolet
  • X-ray spectrum X-ray spectrum
  • Example A 17 includes the method of example A13, further including acquiring hyperspectral image data of the object in at least some of the multiple light spectrums along the coordinated array of sample regions; and analyzing the acquired hyperspectral image data to produce hyperspectral data included in the produced quantitative data set.
  • Example A18 includes the method of example A 13, in which the acquired image data includes metadata associated with each acquired image.
  • Example A19 includes the method of example A 18, in which the metadata includes one or more of a date of image acquisition, a time of the image acquisition, a location of the image acquisition, a temperature around the object, or a humidity around the object.
  • Example A20 includes the method of example A 13, in which the acquiring the image data along the coordinate array of sample regions of the object includes using a multidimensional robotic positioning system coupled to an imager.
  • Example A21 includes the method of example A 13, in which the object includes an artistic work comprising a sculpture, drawing, photograph, or printed material.
  • a system for identifying and authenticating an object includes a data acquisition platform to acquire data from an object; a data processing device, in communication with the data acquisition platform, to analyze the acquired data of the object and produce a quantitative data set including specific characteristics of the object based on the analyzed data, in which the data processing device is configured to identify the object by generating a digital identification based on the specific characteristics that solely corresponds to the object, or to authenticate the object by evaluating the quantitative data set produced for the object by comparing with previously- generated digital identifications to determine an authenticity; and one or more data storage devices, in communication with the data processing device, to store the acquired data and analyzed data.
  • Example A23 includes the system of example A22, in which the data acquisition platform includes a multi- spectral imaging system configured to acquire image data of the object in multiple light spectrums along a coordinated array of sample regions of the object.
  • the data acquisition platform includes a multi- spectral imaging system configured to acquire image data of the object in multiple light spectrums along a coordinated array of sample regions of the object.
  • Example A24 includes the system of example A23, in which the system is configured to determine the authenticity by evaluating the quantitative data set produced for the object by comparing with previously-generated digital identifications stored in the one or more data storage devices to create a validation score, in which the previously-generated digital identifications each comprise a unique multi- spectral fingerprint based on the specific characteristics analyzed from multi- spectral images of corresponding objects, and in which the validation score is indicative of a sufficiency of data to make a comparison of the quantitative data set with the previously-generated digital identifications, and when the validation score is determined to exceed a predetermined threshold, determining a verdict of authenticity of the object to be authentic, inauthentic, or incomplete as matched to a previously-generated identification, and when the validation score is determined to not exceed the predetermined threshold, repeating acquisition of image data to obtain additional multi- spectral image data.
  • Example A25 includes the system of example A22, further including an output device, in communication with the data processing device, to display at least some of the acquired data, the processed data, or both.
  • Example A26 includes the system of example A22, in which the object includes an artistic work comprising a sculpture, drawing, photograph, or printed material.
  • a method for creating an identification for an object includes acquiring image data of an object in multiple electromagnetic (EM) spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set that represents specific characteristics of the object associated with the multiple EM spectrums for each sample region; creating a digital fingerprint that includes the quantitative data set, in which the digital fingerprint solely corresponds to the object; generating an identification corresponding to the digital fingerprint, in which the identification includes an alphanumeric string that is displayable as text, graphic, or audio (or any combination thereof) to identify the object; and storing the digital fingerprint and corresponding identification.
  • EM electromagnetic
  • Example B2 includes the method of example B 1, in which the created digital fingerprint and generated identification are stored in a database, in which the database is also configured to store other identifications and digital fingerprints that solely correspond to other objects.
  • Example B3 includes the method of example B2, further including storing the acquired image data and additional processing data comprising data artifacts in the database.
  • Example B4 includes the method of example B 1, further including acquiring chemical data from the object using an invasive or a noninvasive chemical sampling technique; and analyzing the acquired chemical data to identify additional characteristics of the object; and modifying the quantitative data set to include the additional characteristics.
  • Example B5 includes the method of example B 1, in which the multiple EM spectrums include two or more of an infrared (IR) spectrum, a visible light spectrum, an ultraviolet (UV) spectrum, or an X-ray spectrum.
  • IR infrared
  • UV ultraviolet
  • X-ray spectrum X-ray spectrum
  • Example B6 includes the method of example B 1, further including acquiring hyperspectral image data of the object in at least some of the two or more EM spectrums along the coordinated array of sample regions; and analyzing the acquired hyperspectral image data to produce hyperspectral data included in the produced quantitative data set.
  • Example B7 includes the method of example B 1, in which the digital fingerprint includes one or more data blocks that each includes a respective quantitative data set produced from multiple EM spectral images acquired at a particular point in time for each data block.
  • Example B8 includes the method of example B 1, in which the digital fingerprint includes object information including one or more of an owner name of the object, an author or artist name of the object, a country of origin of the object, provenance information about the object, information about materials in the object, an age of the object, or a state of condition of the object.
  • object information including one or more of an owner name of the object, an author or artist name of the object, a country of origin of the object, provenance information about the object, information about materials in the object, an age of the object, or a state of condition of the object.
  • Example B9 includes the method of example B 1, in which the acquired image data includes metadata associated with each acquired image, and in which the digital fingerprint includes the metadata.
  • Example B 10 includes the method of example B9, in which the metadata includes one or more of a date of image acquisition, a time of the image acquisition, a location of the image acquisition, a temperature around the object, or a humidity around the object.
  • Example B 11 includes the method of example B 1, in which the coordinated array of sample regions of the object includes a contiguous array of the sample regions across a surface of the object, through a depth of the object, or across a surface and through a depth of the object.
  • Example B 12 includes the method of example B 1, in which the coordinated array of sample regions of the object includes a discontiguous array of the sample regions across a surface of the object, through a depth of the object, or across a surface and through a depth of the object.
  • Example B 13 includes the method of example B 1, in which the acquiring the image data along the coordinate array of sample regions of the object includes using a multidimensional robotic positioning system coupled to an imager.
  • Example B 14 includes the method of example B 1, in which the object includes an artistic work comprising a sculpture, drawing, photograph, or printed material.
  • a method for authenticating an object includes acquiring image data of an object in multiple electromagnetic (EM) spectrums at selected sample regions among a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set that represents specific characteristics of the object associated with the multiple EM spectrums for each selected sample region; and evaluating the quantitative data set produced for the object by comparing with previously-generated quantitative data sets associated with other objects stored in a database to determine an authenticity of the object to be the same object as one of the other objects, in which the previously-generated quantitative data sets are each included in a digital fingerprint that solely corresponds to the respective other object, in which the previously- generated quantitative data sets each represents specific characteristics analyzed from images acquired in multiple EM spectrums corresponding to the other objects.
  • EM electromagnetic
  • Example B 16 includes the method of example B 15, in which determining the authenticity of the object includes: creating a validation score that is indicative of a sufficiency of data to make a comparison of the quantitative data set with the previously-generated quantitative data sets; comparing the validation score to a predetermined threshold; and when the validation score meets or exceeds the predetermined threshold, determining a verdict of authenticity of the object to be authentic, inauthentic, or incomplete as matched to a previously- generated quantitative data set.
  • Example B 17 includes the method of example B 16, in which, when the verdict of authenticity of the object is determined to be authentic as matched to a previously-generated digital fingerprint, the method further includes creating a data block that includes the quantitative data set; adding the created data block to the digital fingerprint corresponding to the matched previously-generated digital fingerprint to update the digital fingerprint; and storing the updated digital fingerprint in the database.
  • Example B 18 includes the method of example B 15, in which determining the authenticity of the object includes: creating a validation score that is indicative of a sufficiency of data to make a comparison of the quantitative data set with the previously-generated quantitative data sets; comparing the validation score to a predetermined threshold; and when the validation score does not meet the predetermined threshold, repeating the acquiring image data to obtain additional spectral image data in the multiple EM spectrums.
  • Example B 19 includes the method of example B 15, in which the digital fingerprint includes one or more data blocks that each includes a respective quantitative data set produced from multiple EM spectral images acquired at a particular point in time for each data block.
  • Example B20 includes the method of example B 15, further including storing the acquired image data and additional processing data including data artifacts in the database.
  • Example B21 includes the method of example B 15, further including acquiring chemical data from the object using an invasive or a noninvasive chemical sampling technique; and analyzing the acquired chemical data to identify additional characteristics of the object; and modifying the quantitative data set to include the additional characteristics.
  • Example B22 includes the method of example B 15, in which the multiple EM spectrums include two or more of an infrared (IR) spectrum, a visible light spectrum, an ultraviolet (UV) spectrum, or an X-ray spectrum.
  • IR infrared
  • UV ultraviolet
  • X-ray spectrum X-ray spectrum
  • Example B23 includes the method of example B 15, further including acquiring hyperspectral image data of the object in at least some of the multiple EM spectrums at the at selected sample regions; and analyzing the acquired hyperspectral image data to produce hyperspectral data included in the produced quantitative data set.
  • Example B24 includes the method of example B 15, in which the acquired image data includes metadata associated with each acquired image.
  • Example B25 includes the method of example B24, in which the metadata includes one or more of a date of image acquisition, a time of the image acquisition, a location of the image acquisition, a temperature around the object, or a humidity around the object.
  • Example B26 includes the method of example B 15, in which the coordinated array of sample regions of the object includes a contiguous array of the sample regions across a surface of the object, through a depth of the object, or across a surface and through a depth of the object.
  • Example B27 includes the method of example B 15, in which the coordinated array of sample regions of the object includes a discontiguous array of the sample regions across a surface of the object, through a depth of the object, or across a surface and through a depth of the object.
  • Example B28 includes the method of example B 15, in which the acquiring the image data along the coordinate array of sample regions of the object includes using a multidimensional robotic positioning system coupled to an imager.
  • Example B29 includes the method of example B 15, in which the object includes an artistic work comprising a sculpture, drawing, photograph, or printed material.
  • a system for identifying and authenticating an object includes an image data acquisition platform to acquire image data from an object in multiple electromagnetic (EM) spectrums along a coordinated array of sample regions of the object; a data processing device, in communication with the image data acquisition platform, to analyze the acquired image data of the object and produce a quantitative data set that represents specific characteristics of the object associated with the multiple EM spectrums for each sample region, in which the data processing device is configured to (i) identify the object by creating a digital fingerprint that solely corresponds to the object and includes the quantitative data set, and generate an identification corresponding to the digital fingerprint, in which the identification includes an alphanumeric string that is displayable as text, graphic, or audio to identify the object, or (ii) authenticate the object by evaluating the quantitative data set produced for the object by comparing with previously-generated
  • EM electromagnetic
  • Example B31 includes the system of example B30, in which the system is configured to determine the authenticity by evaluating the quantitative data set produced for the object by comparing with the previously-generated quantitative data sets corresponding to the other objects stored in the one or more data storage devices to create a validation score, in which the previously-generated quantitative data sets are each included in a digital fingerprint that solely corresponds to the respective other object, and in which the validation score is indicative of a sufficiency of data to make a comparison of the quantitative data set with the previously- generated quantitative data sets.
  • Example B32 includes the system of example B31, in which the system is configured to compare the validation score to a predetermined threshold to determine whether the validation score exceeds the predetermined threshold, and when the validation score is determined to exceed the predetermined threshold, the system is configured to determine a verdict of authenticity of the object to be authentic, inauthentic, or incomplete as matched to a previously- generated quantitative data sets.
  • Example B33 includes the system of example B31, in which the system is configured to compare the validation score to a predetermined threshold to determine whether the validation score exceeds the predetermined threshold, and when the validation score is determined to not exceed the predetermined threshold, the system is configured to repeat acquisition of the image data to obtain additional spectral image data in the multiple EM spectrums.
  • Example B34 includes the system of example B30, in which the digital fingerprint includes one or more data blocks each comprising a quantitative data set produced for a multiple EM spectral imaging data set acquired at a particular point in time.
  • Example B35 includes the system of example B30, in which the digital fingerprint includes object information including one or more of an owner name of the object, an author or artist name of the object, a country of origin of the object, provenance information about the object, information about materials in the object, an age of the object, or a state of condition of the object.
  • object information including one or more of an owner name of the object, an author or artist name of the object, a country of origin of the object, provenance information about the object, information about materials in the object, an age of the object, or a state of condition of the object.
  • Example B36 includes the system of example B30, further including an output device, in communication with the data processing device, to display at least some of the acquired data, the processed data, or both.
  • Example B37 includes the system of example B30, in which the output device includes one or more of a smartphone, a tablet, a wearable computing device, or a personal computer including a desktop or laptop computer.
  • the output device includes one or more of a smartphone, a tablet, a wearable computing device, or a personal computer including a desktop or laptop computer.
  • Example B38 includes the system of example B30, in which the object includes an artistic work comprising a sculpture, drawing, photograph, or printed material.
  • Example B39 includes the system of example B30, in which the image data acquisition platform includes a multi-dimensional robotic positioning system coupled to an imager to acquire the image data along the coordinate array of sample regions of the object.
  • the image data acquisition platform includes a multi-dimensional robotic positioning system coupled to an imager to acquire the image data along the coordinate array of sample regions of the object.
  • Example B40 includes the system of example B39, in which the image data acquisition platform includes a lighting bracket coupled to the robotic positioning system that moves with the imager to ensure the same lighting conditions for each image acquired along the coordinate array of sample regions of the object.
  • Implementations of the subject matter and the functional operations described in this patent document can be implemented in various systems, digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.
  • Implementations of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-transitory computer readable medium for execution by, or to control the operation of, data processing apparatus.
  • the computer readable medium can be a machine- readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them.
  • the term "data processing unit” or “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a
  • the apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
  • a computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • a computer program does not necessarily correspond to a file in a file system.
  • a program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code).
  • a computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
  • the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output.
  • the processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
  • processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
  • a processor will receive instructions and data from a read only memory or a random access memory or both.
  • the essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.
  • a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • a computer need not have such devices.
  • Computer readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices.
  • semiconductor memory devices e.g., EPROM, EEPROM, and flash memory devices.
  • the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
  • the disclosed system and methods address device-centric challenges of examining physical properties of materials and tangible objects, with some examples including paintings, sculptures, drawings and other artistic works and high-end articles. Aspects of the disclosed methods are necessarily rooted in computer technology to specifically overcome problems of identifying, characterizing and classifying unique physical features of such materials and objects, as well as to improve the functioning of the analysis instruments, by using computerized analytical processing techniques.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Signal Processing (AREA)
  • Probability & Statistics with Applications (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne des systèmes, des dispositifs et des procédés permettant de quantifier des caractéristiques uniques d'un objet tel qu'un travail artistique afin d'identifier et d'authentifier l'objet et des caractéristiques spécifiques de ce dernier à l'aide de techniques de caractérisation de diagnostic multi-spectral et d'algorithmes analytiques. Selon certains aspects, un procédé de création d'une identification d'un objet consiste à acquérir des données d'image d'un objet dans au moins deux spectres électromagnétiques le long d'un réseau coordonné de zones d'échantillon de l'objet ; à analyser les données d'image acquises afin de produire un ensemble de données quantitatives comprenant des caractéristiques spécifiques de l'objet associé auxdits spectres électromagnétiques pour chaque zone d'échantillon ; à générer une identification numérique associée à une empreinte digitale de données unique, en fonction des caractéristiques spécifiques, l'identification numérique correspondant uniquement à l'objet ; et à mémoriser l'identification numérique générée.
PCT/US2018/055076 2017-10-09 2018-10-09 Systèmes et procédés d'identification et d'authentification de travaux artistiques WO2019074967A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
US16/754,875 US11113554B2 (en) 2017-10-09 2018-10-09 Systems and methods for identifying and authenticating artistic works
EP18866860.2A EP3695379A4 (fr) 2017-10-09 2018-10-09 Systèmes et procédés d'identification et d'authentification de travaux artistiques
US17/404,429 US11756322B2 (en) 2017-10-09 2021-08-17 Systems and methods for identifying and authenticating artistic works
US18/361,165 US20240161526A1 (en) 2017-10-09 2023-07-28 Systems and methods for identifying and authenticating artistic works

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201762569932P 2017-10-09 2017-10-09
US62/569,932 2017-10-09

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US16/754,875 A-371-Of-International US11113554B2 (en) 2017-10-09 2018-10-09 Systems and methods for identifying and authenticating artistic works
US17/404,429 Continuation US11756322B2 (en) 2017-10-09 2021-08-17 Systems and methods for identifying and authenticating artistic works

Publications (1)

Publication Number Publication Date
WO2019074967A1 true WO2019074967A1 (fr) 2019-04-18

Family

ID=66101083

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2018/055076 WO2019074967A1 (fr) 2017-10-09 2018-10-09 Systèmes et procédés d'identification et d'authentification de travaux artistiques

Country Status (3)

Country Link
US (3) US11113554B2 (fr)
EP (1) EP3695379A4 (fr)
WO (1) WO2019074967A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021197510A1 (fr) * 2020-03-31 2021-10-07 吉林求是光谱数据科技有限公司 Système d'identification d'empreinte digitale de téléphone mobile ayant une fonction d'identification d'empreinte digitale authentique/fausse, et procédé d'identification
CN114817614A (zh) * 2022-05-18 2022-07-29 深圳市高光谱新技术有限公司 防伪标记的生成方法、识别方法和生成系统、识别系统、智能终端及可读存储介质

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11341459B2 (en) 2017-05-16 2022-05-24 Artentika (Pty) Ltd Digital data minutiae processing for the analysis of cultural artefacts
EP3695379A4 (fr) 2017-10-09 2021-07-07 Great Masters Art Authentication, LLC Systèmes et procédés d'identification et d'authentification de travaux artistiques
US11201726B2 (en) * 2019-05-02 2021-12-14 International Business Machines Corporation Multi-layered image encoding for data block
US11543360B2 (en) * 2019-07-02 2023-01-03 Cortica Ltd. Identifying and grading diamonds
US11838400B2 (en) * 2019-11-19 2023-12-05 International Business Machines Corporation Image encoding for blockchain
US11328170B2 (en) * 2020-02-19 2022-05-10 Toyota Research Institute, Inc. Unknown object identification for robotic device
JP7363589B2 (ja) * 2020-03-04 2023-10-18 トヨタ自動車株式会社 塗装品質予測装置および学習済みモデルの生成方法
US11079278B1 (en) * 2020-12-22 2021-08-03 Illuscio, Inc. Systems and methods for using multispectral imagery for precise tracking and verification
EP4323758A1 (fr) * 2021-04-12 2024-02-21 LDVS, Inc. Surveillance d'états et d'état de santé d'oeuvres artistiques
CN112990095B (zh) * 2021-04-13 2021-09-14 广州市玄武无线科技股份有限公司 商品陈列分析方法、装置、设备及存储介质
US20230117616A1 (en) * 2021-10-18 2023-04-20 Meta Platforms, Inc. Searching for products through a social media platform
US11948386B1 (en) 2023-07-06 2024-04-02 SpaceFarm LLC Artwork authenticity systems and methods

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080212840A1 (en) * 2006-09-12 2008-09-04 Tamir Shalom Imaging system, method, and accessory therefor
US20110127432A1 (en) * 2007-01-30 2011-06-02 New Jersey Institute Of Technology Methods and apparatus for the non-destructive detection of variations in a sample
US20120175866A1 (en) * 2009-07-14 2012-07-12 Friedrich Kisters Security element for marking or identifying objects and living beings
US20170263011A1 (en) * 2015-09-25 2017-09-14 A9.Com, Inc. Material recognition for object identification

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2175396A3 (fr) * 2005-12-23 2010-08-11 Ingenia Holdings (UK) Limited Authentification optique
US20070247317A1 (en) 2006-04-21 2007-10-25 Rick Farrell Apparatus and method for fine art authentication
DE102008018931A1 (de) 2007-04-17 2008-11-13 Gyrus ACMI, Inc., Southborough Lichtquellenleistung auf der Grundlage einer vorbestimmten erfaßten Bedingung
US8194933B2 (en) * 2007-12-12 2012-06-05 3M Innovative Properties Company Identification and verification of an unknown document according to an eigen image process
US8624727B2 (en) 2008-01-28 2014-01-07 Saigh And Son, Llc Personal safety mobile notification system
US20100097208A1 (en) 2008-10-20 2010-04-22 G-Tracking, Llc Method and System for Tracking Assets
WO2014047078A1 (fr) 2012-09-18 2014-03-27 Vootner Goushe Llc Système de détection pour la protection d'œuvres artistiques et d'autres objets de valeur
EP3695379A4 (fr) 2017-10-09 2021-07-07 Great Masters Art Authentication, LLC Systèmes et procédés d'identification et d'authentification de travaux artistiques

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080212840A1 (en) * 2006-09-12 2008-09-04 Tamir Shalom Imaging system, method, and accessory therefor
US20110127432A1 (en) * 2007-01-30 2011-06-02 New Jersey Institute Of Technology Methods and apparatus for the non-destructive detection of variations in a sample
US20120175866A1 (en) * 2009-07-14 2012-07-12 Friedrich Kisters Security element for marking or identifying objects and living beings
US20170263011A1 (en) * 2015-09-25 2017-09-14 A9.Com, Inc. Material recognition for object identification

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021197510A1 (fr) * 2020-03-31 2021-10-07 吉林求是光谱数据科技有限公司 Système d'identification d'empreinte digitale de téléphone mobile ayant une fonction d'identification d'empreinte digitale authentique/fausse, et procédé d'identification
CN114817614A (zh) * 2022-05-18 2022-07-29 深圳市高光谱新技术有限公司 防伪标记的生成方法、识别方法和生成系统、识别系统、智能终端及可读存储介质

Also Published As

Publication number Publication date
US20210374449A1 (en) 2021-12-02
US20240161526A1 (en) 2024-05-16
US20200311452A1 (en) 2020-10-01
EP3695379A1 (fr) 2020-08-19
US11756322B2 (en) 2023-09-12
EP3695379A4 (fr) 2021-07-07
US11113554B2 (en) 2021-09-07

Similar Documents

Publication Publication Date Title
US11756322B2 (en) Systems and methods for identifying and authenticating artistic works
US11843709B2 (en) Secure digital fingerprint key object database
US11805135B2 (en) Distributed transaction-based security and tracking of machine and agronomic data
US11321772B2 (en) Systems and methods for identification document processing and business workflow integration
FR3129006A1 (fr) Procédé de fourniture de service d’authentification d’actif réel à l’aide d’un identifiant décentralisé et d’un jeton non fongible
CN110770711B (zh) 基于图像的伪造品检测
US20100241528A1 (en) Anti-counterfeiting system and method
CN111406270B (zh) 基于图像的伪造品检测
KR20170001849A (ko) 정품인증 수단, 정품인증 시스템
EP3258410A1 (fr) Appareil de traitement d'informations et procédé de traitement d'informations
US20220222760A1 (en) Asset Verification Systems and/or Methods
KR20130141403A (ko) 미술품의 특성을 이용한 미술품 감정 방법 및 시스템
Özkan et al. Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples
US10192089B1 (en) Systems and methods for authentication of consumer products
AU2015363041A1 (en) Batch authentication of materials for automated anti counterfeiting
US20170310664A1 (en) Apparatus and system for obtaining and encrypting documentary materials
US20230375476A1 (en) System and method for assessing product
US20100188512A1 (en) Method and system for testing image pipelines
Filippi et al. Hyperspectral agricultural mapping using support vector machine-based endmember extraction (SVM-BEE)
CA3223108A1 (fr) Classification utilisant des strategies d'intelligence artificielle qui reconstruisent des donnees a l'aide de transformations de compression et de decompression
TWI754972B (zh) 影像驗證方法及產品即時認證系統
POKROVSKAYA The Role of AI in Protecting Intellectual Property Rights on E-Commerce Marketplaces
Wood et al. Predicting Counterfeits from Smartphone Multi-Images Using Deep Learning
Chen et al. The Rich, the Poor, and the Ugly: An Aesthetic-Perspective Assessment of NFT Values
Viswanatha et al. Online Fraud Detection Using Machine Learning Approach

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18866860

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2018866860

Country of ref document: EP

Effective date: 20200511